And automated adaptation (continual learning, test time training) should enable a lot of serial time that would overcome even issues with splitting a problem into subproblems (it’s not necessarily possible to solve a 10-year problem in 2 years with any number of competent researchers and managers). So to the extent in-context learning implements continual learning, presence of any visible bounds on time horizons in capabilities indicates and quantifies limitations of how well it actually does implement continual learning. A genuine advancement in continual learning might well immediately do away with any time horizons entirely.
And automated adaptation (continual learning, test time training) should enable a lot of serial time that would overcome even issues with splitting a problem into subproblems (it’s not necessarily possible to solve a 10-year problem in 2 years with any number of competent researchers and managers). So to the extent in-context learning implements continual learning, presence of any visible bounds on time horizons in capabilities indicates and quantifies limitations of how well it actually does implement continual learning. A genuine advancement in continual learning might well immediately do away with any time horizons entirely.